Menu

Month: February 2012

Flirting is exhausting. It requires you to control your behaviour (‘Don’t act like a moron!’), monitor the impression you are making (‘Are we laughing at the joke or at me?’) and communicate on several different layers (‘Do you want to come up for tea?’). No wonder men’s cognitive performance is worse afterwards. Surprisingly though, the exhaustion sets in a lot earlier already: before men even know who the woman may be.

Nauts and colleagues have a (freely available) article in press at the moment which looks at the cognitive performance of men and women as a result of being observed by an unknown male of female experimenter. In a first experiment, they asked participants to read words out loud while either ostensibly being monitored by ‘Bas’ (a fake male experimenter) or ‘Lisa’ (a fake female experimenter). Importantly, participants only knew the names of their observers. They never saw them or directly interacted with them. It must have looked like your typical unsexy psychology experiment in which boredom is the unmeasured main effect.

Before and after reading words out loud, people performed a Stroop task. Basically, it asks you to name the ink colour of coloured colour words, e.g. people are quite fast on RED, GREEN, and BLUE but usually slower on RED, GREEN, and BLUE. This difference is sometimes taken as an indicator of people’s ability to overcome an easy, fast response due to attention on the word meaning. As predicted, men’s Stroop performance was significantly worse after having been ‘observed’ by ‘Lisa’ compared to ‘Bas’, i.e. their cognitive performance on a standard control task was worse just because they felt that a completely unknown woman (they would likely never meet) had a look at their mouth while pronouncing words.

In a follow-up experiment participants were told about the upcoming word reading task – and the gender of the experimenter who would observe them – already before the Stroop task was performed for the first time. As predicted, men’s Stroop performance was somewhat worse when merely expecting to be observed by an unknown female experimenter compared to a male one [1]. This suggests that the preparation for being observed by a woman is enough to cognitively exhaust men.

Interestingly, in neither experiment women’s cognitive performance was affected by the gender of the previous (Experiment 1) or upcoming (Experiment 2) observer. Mind that the statistical power was greater for women given that more were tested. In other words, the experiment was more likely to find a female participant effect than a male participant effect. The authors suggest that women are generally more selective in terms of seeing a situation as flirtatious or not. Men on the other hand, well, are less selective.

So, the exhaustion of flirting appears to start even before the flirt, or even before a situation which could possibly lead at some point to a flirt. While the authors are right in writing that a replication with homosexual participants would be interesting, the far more obvious short coming lies elsewhere.

Nauts et al. did not just test ‘men’ and ‘women’ but 21 year old students. To put it more bluntly, they arguably recruited the most horny people on campus. So, this study is not just an example of young men’s readiness to expand cognitive resources on a completely unlikely mating partner. It is also an example of the need for Psychology to go beyond student samples in order to truly reveal something about human nature.

How come my Nokia text messaging programme prefers to turn my pressing of 782 into ‘rub’ first and only then offers me ‘pub’? Surely, the dictionaries which predictive texting programmes use can be improved. The only question is how. Psycholinguistics may have found the answer on the television screen.

The basis on which the initial texting dictionaries are compiled has not been disclosed. Still, the order in which alternatives are offered suggests that it is out of date. Below is an indication of why ‘rub’ is offered first and ‘pub’ thereafter: that’s how their respective frequencies (vertical axis) used to be ordered. Google’s ngram viewer, however, suggests that by 1980 (time is on the horizontal axis) ‘pub’ overtook ‘rub’. Other such word pairs also switched order of use during the twentieth century: ‘boy’/‘box’ (1990), ‘rope’/‘pose’ (1983), ‘lord’/‘lose’ (1904), ‘Ford’/‘dose’ (1951).

Still, predictive texting does not only face the challenge of how to order suggestions. It also has to limit the choices it offers for memory and usability reasons. For example, for 236737 my mobile phone does know ‘afores’ but not ‘adorer’. The challenge of which words to include is faced by all dictionaries. The Oxford Dictionary (LINK) uses a huge collection of texts called the Oxford English Corpus and includes new words if there is evidence that they are significant or important. I suspect that the most crucial criterion is simply how many times a word is used.

But which corpus is the best to determine that? Surely, for text messaging one would prefer a corpus made up only of text messages. In the absence of such corpora for all the languages text messaging is used in, one may turn to big corpora under the assumption that size matters or one may turn to corpora of sources users are likely to read, e.g. the internet. But how do you arbitrate between these options? Ideally, one would like to simply try out different approaches in real world text messaging and see how they perform. For the moment, however, it is easier to turn to what we already know from psycholinguistic research about word frequencies.

Psycholinguists have been interested in word frequencies for a long time because they account for up to 40% of the reading speed, as measured by how long it takes people to judge whether a letter string is a word or not (Brysbaert et al., 2011). Given that reading speed is often taken as a proxy for how words a represented in the mental lexicon, controlling for frequency is now standard practice in Psycholinguistics. Recently, Brysbaert and colleagues (2011) pitted different corpora against each other in order to see which one accounts best for reading speed. Google’s scanning of words resulted in the biggest corpus in the study but it did not perform the best. Even limiting the google corpus to more recent books did not improve its performance enough to outperform its unlikely rival which is 99.9% smaller: subtitles.

Whether in English, French, German or Chinese, word frequencies based on subtitles outperform their rivals based on books. They account for more variance in word judgement times in both typical, young research participants as well as old people in their seventies (Brysbaert et al., 2011). Furthermore, corpora based on subtitles have the added benefit of being easy to compile as well as update and they could be made available in all televised languages.

Too many people waste too much time correcting predicted words to ignore the need for better dictionaries. Given the strong psycholinguistic support for subtitle based word frequencies, it would be worth trying them out in predictive texting programmes. Perhaps this way ‘rub’ and ‘pub’ will be offered in the right order and 236737 will no longer be an ‘afores’ but instead an ‘adorer’.